The Impact of Information and Communication Technology on Commercial Banks’ Performance: Evidence from MENA
Bibliographic record
Abstract
The importance of information in achieving different organizational goals cannot be overstated since it ensures the rapid distribution of resources required to achieve desirable goals. The banking industry’s environment is incredibly dynamic and undergoes quick changes because of creativity, innovation, technological advancements, altered perceptions, and customer expectations. The center of the change curve is information and communication technology (ICT). Business organizations, particularly those in the banking sector, operate in a complex and competitive environment defined by shifting conditions and a volatile economic climate. Data for 20 MENA countries has been collected from the World Bank database between 1997 and 2021. Two-step System (Generalized Method of Moments) GMM were used to evaluate the influence of intrinsic features of individuals in a panel data set and avoid bias caused by omitted variables. The impact of the relationship between banks' performance and their use of ICT was evaluated in this study. The data analysis revealed that the impact of ICT on bank performance in MENA is positive. This suggests that a little shift in the banking industry's investment and adoption of ICT will result in a corresponding rise in profit levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".